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This paper reports on a large-scale, end-to- end relation and event extraction system. At present, the system extracts a total of 100 types of relations and events, which represents a …...

. We propose a tree-similarity-based unsupervised learning method to extract relations between Named Entities from a large raw corpus. Our method regards relation extraction as a clustering problem on shallow …...

. This paper presents a supervised approach for relation extraction. We apply Support Vector Machines to detect and classify the relations in Automatic Content Extraction (ACE) corpus. We use a …...

This paper proposes a novel composite ker- nel for relation extraction. The composite kernel consists of two individual kernels: an entity kernel that allows for entity-related features and a convolution …...

We propose an approach for extracting re- lations between entities from biomedical literature based solely on shallow linguis- tic information. We use a combination of kernel functions to integrate two …...

This paper proposes to use a convolution kernel over parse trees to model syntactic structure information for relation extrac- tion. Our study reveals that the syntactic structure features embedded in …...

Unsupervised paraphrase acquisition has been an active research field in recent years, but its effective coverage and per- formance have rarely been evaluated. We propose a generic paraphrase-based ap- proach …...

We present an approach for the joint ex- traction of entities and relations in the con- text of opinion recognition and analysis. We identify two types of opinion-related entities — …...

This paper proposes a novel hierarchical learn- ing strategy to deal with the data sparseness problem in relation extraction by modeling the commonality among related classes. For each class in …...

Relation extraction is the task of find- ing semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have …...

We present a new approach to relation ex- traction that requires only a handful of train- ing examples. Given a few pairs of named entities known to exhibit or not …...

. This paper proposes a convolution tree kernel-based approach for relation extraction where the parse tree is expanded with entity features such as entity type, subtype, and mention level etc. …...

We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of …...

This paper discusses local alignment ker- nels in the context of the relation extrac- tion task. We define a local alignment kernel based on the Smith-Waterman mea- sure as a …...

We propose a method that incorporates paraphrase information from the Web to boost the performance of a supervised re- lation extraction system. Contextual infor- mation is extracted from the Web …...

We propose a new integrated approach based on Markov logic networks (MLNs), an effective com- bination of probabilistic graphical models and first- order logic for statistical relational learning, to ex- …...

Paraphrases have proved to be useful in many applications, including Machine Translation, Question Answering, Summarization, and In- formation Retrieval. Paraphrase acquisition methods that use a single monolingual corpus often produce …...

This paper explores the use of innovative kernels based on syntactic and semantic structures for a target relation extraction task. Syntax is derived from constituent and dependency parse trees whereas …...

This paper addresses question analysis in the framework of Question Answering over struc- tured data. The problem is set as a relation extraction task, where all the relations of inter- …...

In this paper, we investigate an unsupervised approach to Relation Extraction to be applied in the context of automatic generation of multiple-choice questions (MCQs). The approach aims to identify the …...

While extensive studies on relation ex- traction have been conducted in the last decade, statistical systems based on su- pervised learning are still limited because they require large amounts of …...

AbstractA variety of methods exist for extracting terms and relations between terms from a corpus, each of them having strengths and weaknesses. Rather than just using the joint results, we …...

We present a novel approach to relation ex- traction that integrates information across doc- uments, performs global inference and re- quires no labelled text. In particular, we tackle relation extraction …...

This paper proposes a convolution forest ker- nel to effectively explore rich structured fea- tures embedded in a packed parse forest. As opposed to the convolution tree kernel, the proposed …...

Relations between entities in text have been widely researched in the natu- ral language processing and information- extraction communities. The region con- necting a pair of entities (in a parsed …...

Relation extraction is the task of recog- nizing semantic relations among entities. Given a particular sentence supervised ap- proaches to Relation Extraction employed feature or kernel functions which usu- ally …...

In this paper, we provide a theoretical framework for feature selection in tree ker- nel spaces based on gradient-vector com- ponents of kernel-based machines. We show that a huge number …...

AbstractSynonyms dictionaries are useful resources for natural language processing. Unfortunately their availability in digital format is limited, as publishing companies do not release their dictionaries in open digital formats. Dicionário-Aberto …...

A great deal of information on the Web is represented in both textual and structured form. The structured form is machine- readable and can be used to augment the textual …...

AbstractDespite the large variety of corpora in the biomedical domain their annotations differ in many respects, e.g., the coverage of different, highly specialized knowledge domains, varying degrees of granularity of …...

AbstractThis paper describes a multi-lingual large-scale concept network obtained automatically by mining for concepts and relations and exploiting a variety of sources of knowledge from Wikipedia. Concepts and their lexicalizations …...

Traditional approaches to Relation Extraction from text require manually defining the rela- tions to be extracted. We propose here an ap- proach to automatically discovering relevant relations, given a large …...

In this paper, we observe that there exists a second dimension to the relation extraction (RE) problem that is orthogonal to the relation type dimension. We show that most of …...

Previous work on relation extraction has focussed on identifying relationships be- tween entities that occur in the same sentence (intra-sentential relations) rather than between entities in different sen- tences (inter-sentential …...

. In this paper, we focus on the task of distilling relation instances from the Web. Most of the approaches for this task were based on provided seed instances or …...

This paper investigates the application of an existing seed-based minimally super- vised learning algorithm to different so- cial domains exhibiting different proper- ties of the available data. A systematic analysis …...

In this paper we present an information service system that allows users to search for the key players of requested technol- ogy areas and for their collaboration net- works. This …...

AbstractRelation extraction (RE) is an important text mining task which is the basis for further complex and advanced tasks. In state-of-the-art RE approaches, syntactic information obtained through parsing plays a …...

AbstractThis paper presents an approach to construction of an annotated corpus for German political news for the opinion mining task. The annotated corpus has been applied to learn relation extraction …...

Kernel based methods dominate the current trend for various relation extraction tasks including protein-protein interaction (PPI) extraction. PPI information is critical in un- derstanding biological processes. Despite considerable efforts, previously …...

The well-studied supervised Relation Extraction algorithms require training data that is accurate and has good coverage. To obtain such a gold standard, the common practice is to do independent double …...

. In this article, we focus on the problem of adapting an open-domain question- answering system to a specialized domain, the medical domain in the present case with family doctors …...

AbstractUnsupervised methods gain more and more attention nowadays in information extraction area, which allows to design more open extraction systems. In the domain of unsupervised information extraction, clustering methods are …...

In this paper, we study how to automatically extract and visualize food (or nutrition) and disease relationships from Chinese publications of Nutritional Genomics. Different from previous approaches that mostly apply …...

Argumentative discourse contains not only language expressing claims and evidence, but also language used to organize these claims and pieces of evidence. Differentiating be- tween the two may be useful …...

Many NLP tasks make predictions that are in- herently coupled to syntactic relations, but for many languages the resources required to pro- vide such syntactic annotations are unavail- able. For …...

This paper proposes a novel approach for rela- tion extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is …...

Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela- tion extraction are mostly supervised. The clear drawback of supervised methods is …...

This paper presents an approach that exploits the scope of negation cues for relation extrac- tion (RE) without the need of using any specif- ically annotated dataset for building a …...

Advances in sentiment analysis have enabled extraction of user relations implied in online textual exchanges such as forum posts. How- ever, recent studies in this direction only con- sider direct …...

Traditional relation extraction predicts rela- tions within some fixed and finite target schema. Machine learning approaches to this task require either manual annotation or, in the case of distant supervision, …...

Semantic relation clustering for unsupervised information extraction Most studies in unsupervised information extraction concentrate on the relation extraction and few work has been proposed on the organization of the extracted …...

A promising approach to relation extrac- tion, called weak or distant supervision, exploits an existing database of facts as training data, by aligning it to an unla- beled collection of …...

We present a weakly-supervised induc- tion method to assign semantic informa- tion to food items. We consider two tasks of categorizations being food-type classi- fication and the distinction of whether …...

Most existing relation extraction models make predictions for each entity pair lo- cally and individually, while ignoring im- plicit global clues available in the knowl- edge base, sometimes leading to …...

Despite the successes of distant supervision approaches to relation extraction in the news do- main, the lack of a comprehensive ontology of medical relations makes it difficult to apply such …...

In this paper, we propose and demonstrate Exploratory Relation Extraction (ERE), a novel approach to identifying and extracting relations from large text corpora based on user-driven and data-guided incremental exploration. …...

We propose a two-phase framework to adapt existing relation extraction classi- fiers to extract relations for new target do- mains. We address two challenges: neg- ative transfer when knowledge in …...

Most previous work in information extraction from text has focused on named-entity recognition, entity linking, and relation extraction. Less attention has been paid given to extracting the temporal scope for …...

Hypernymy relation acquisition has been widely investigated, especially because tax- onomies, which often constitute the backbone structure of semantic resources are structured using this type of relations. Although lots of …...

This paper contributes a joint embedding model for predicting relations between a pair of entities in the scenario of rela- tion inference. It differs from most stand- alone approaches which …...

Representation learning of knowledge bases aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step …...

Open domain relation extraction systems iden- tify relation and argument phrases in a sen- tence without relying on any underlying schema. However, current state-of-the-art re- lation extraction systems are available …...

We describe a novel max-margin learn- ing approach to optimize non-linear perfor- mance measures for distantly-supervised re- lation extraction models. Our approach can be generally used to learn latent variable …...

Relation extraction is the task of ex- tracting predicate-argument relationships between entities from natural language text. This paper investigates whether back- ground information about entities avail- able in knowledge bases …...

AbstractThe task of Relation Extraction from texts is one of the main challenges in the area of Information Extraction, considering the required linguistic knowledge and the sophistication of the language …...

Kernel methods have been widely stud- ied in several natural language process- ing tasks such as relation extraction and sentence classification. In this work, we present a new graph kernel …...

AbstractHypernym-hyponym (“is-a”) relations are key components in taxonomies, object hierarchies and knowledge graphs. While there is abundant research on is-a relation extraction in English, it still remains a challenge to …...

Modern NLP models rely heavily on engi- neered features, which often combine word and contextual information into complex lexi- cal features. Such combination results in large numbers of features, which …...

AbstractEvent extraction is a difficult information extraction task. Li et al. (2014) explore the benefits of modeling event extraction and two related tasks, entity mention and relation extraction, jointly. This …...

SemEval 2016 Task 12 addresses temporal reasoning in the clinical domain. In this paper, we present our participation for relation ex- traction based on gold standard entities (sub- tasks DR …...

Distant supervised relation extraction has been widely used to find novel relational facts from text. However, distant su- pervision inevitably accompanies with the wrong labelling problem, and these noisy data …...

Existing knowledge-based question an- swering systems often rely on small an- notated training data. While shallow meth- ods like relation extraction are robust to data scarcity, they are less expressive …...

AbstractDistant supervision is an efficient approach that automatically generates labeled data for relation extraction (RE). Traditional distantly supervised RE systems rely heavily on handcrafted features, and hence suffer from error …...

AbstractRecent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data. We describe our approach for combining two English resources, FrameNet and sar-graphs, and …...

AbstractThe task of relation extraction is to recognize and extract relations between entities or concepts in texts. Dependency parse trees have become a popular source for discovering extraction patterns, which …...

Relation extraction models based on deep learning have been attracting a lot of at- tention recently. Little research is carried out to reduce their need of labeled training data. In …...

AbstractAdversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data. We apply adversarial training in relation extraction within the multi-instance multi-label learning framework. …...

AbstractThough feature extraction is a necessary first step in statistical NLP, it is often seen as a mere preprocessing step. Yet, it can dominate computation time, both during training, and …...

AbstractPast work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary …...

AbstractNeural networks have shown promising results for relation extraction. State-of-the-art models cast the task as an end-to-end problem, solved incrementally using a local classifier. Yet previous work using statistical models …...

AbstractRelation extraction is the task of recognizing and extracting relations between entities or concepts in texts. A common approach is to exploit existing knowledge to learn linguistic patterns expressing the …...

AbstractWe introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for …...

AbstractRelation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, …...

AbstractSlot Filling (SF) aims to extract the values of certain types of attributes (or slots, such as person:cities_of_residence) for a given entity from a large collection of source documents. In …...

AbstractDistant supervision significantly reduces human efforts in building training data for many classification tasks. While promising, this technique often introduces noise to the generated training data, which can severely affect …...

AbstractThis paper investigates how to improve performance on information extraction tasks by constraining and sequencing CRF-based approaches. We consider two different relation extraction tasks, both from the medical literature: dependence …...

AbstractRelation extraction has been widely used for finding unknown relational facts from plain text. Most existing methods focus on exploiting mono-lingual data for relation extraction, ignoring massive information from the …...

AbstractIn this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing …...

AbstractOrganized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly …...

AbstractWe show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot. This reduction has several advantages: …...

Monitoring mobility- and industry-relevant events is important in areas such as personal travel planning and supply chain management, but extracting events pertaining to specific companies, transit routes and locations from …...

AbstractAutomatic extraction of semantic relations from text can support finding relevant information from scientific publications. We describe our participation in Task 7 of SemEval-2018 for which we experimented with two …...

Relational nouns refer to an entity by virtue of how it relates to another entity. Their identification in text is a prerequisite for the correct semantic interpretation of a sentence, …...

AbstractModeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, …...

AbstractReliably detecting relevant relations between entities in unstructured text is a valuable resource for knowledge extraction, which is why it has awaken significant interest in the field of Natural Language …...

AbstractFundamental to several knowledge-centric applications is the need to identify named entities from their textual mentions. However, entities lack a unique representation and their mentions can differ greatly. These variations …...

The increased demand for structured knowledge has created considerable interest in relation extraction (RE) from large collections of documents. In particular, distant supervision can be used for RE without manual …...

AbstractOpen Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks …...

AbstractMost work in relation extraction forms a prediction by looking at a short span of text within a single sentence containing a single entity pair mention. This approach often does …...

Alignments between natural language and Knowledge Base (KB) triples are an essential prerequisite for training machine learning approaches employed in a variety of Natural Language Processing problems. These include Relation …...

AbstractIn this work we evaluate applicability of entity pair models and neural network architectures for relation extraction and classification in scientific papers at SemEval-2018. We carry out experiments with representing …...

Museums and other cultural heritage institutions have large databases of information about the objects in their collections, and existing Natural Language Generation (NLG) systems can generate fluent and adaptive texts …...

AbstractDependency trees convey rich structural information that is proven useful for extracting relations among entities in text. However, how to effectively make use of relevant information while ignoring irrelevant information …...

AbstractMost existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, …...

AbstractMany existing relation extraction (RE) models make decisions globally using integer linear programming (ILP). However, it is nontrivial to make use of integer linear programming as a blackbox solver for …...

AbstractRelation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities …...

AbstractWe examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by …...

AbstractResearchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words …...

AbstractIn practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context …...

AbstractIn order to automate banking processes (e.g. payments, money transfers, foreign trade), we need to extract banking transactions from different types of mediums such as faxes, e-mails, and scanners. Banking …...

AbstractDistant supervision has been widely used in relation extraction tasks without hand-labeled datasets recently. However, the automatically constructed datasets comprise numbers of wrongly labeled negative instances due to the incompleteness …...

AbstractIn this paper, we propose a novel graph neural network with generated parameters (GP-GNNs). The parameters in the propagation module, i.e. the transition matrices used in message passing procedure, are …...

AbstractDistantly-supervised relation extraction has proven to be effective to find relational facts from texts. However, the existing approaches treat labels as independent and meaningless one-hot vectors, which cause a loss …...

AbstractWe address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of …...

AbstractRecently we proposed the Span Attribute Tagging (SAT) Model to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the challenge of large label space and limited …...

AbstractIn recent years there is a surge of interest in applying distant supervision (DS) to automatically generate training data for relation extraction (RE). In this paper, we study the problem …...

AbstractGeneral purpose relation extractors, which can model arbitrary relations, are a core aspiration in information extraction. Efforts have been made to build general purpose extractors that represent relations with their …...

AbstractWe study relation extraction for knowledge base (KB) enrichment. Specifically, we aim to extract entities and their relationships from sentences in the form of triples and map the elements of …...

AbstractRelation extraction (RE) aims to label relations between groups of marked entities in raw text. Most current RE models learn context-aware representations of the target entities that are then used …...

AbstractDistant supervision (DS) is an important paradigm for automatically extracting relations. It utilizes existing knowledge base to collect examples for the relation we intend to extract, and then uses these …...

AbstractRelation Extraction is the task of identifying entity mention spans in raw text and then identifying relations between pairs of the entity mentions. Recent approaches for this span-level task have …...

AbstractRelation Extraction (RE) consists in detecting and classifying semantic relations between entities in a sentence. The vast majority of the state-of-the-art RE systems relies on morphosyntactic features and supervised machine …...

AbstractNature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts …...

AbstractMultilingual pre-trained Transformers, such as mBERT (Devlin et al., 2019) and XLM-RoBERTa (Conneau et al., 2020a), have been shown to enable effective cross-lingual zero-shot transfer. However, their performance on Arabic …...

AbstractThis paper proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the form of a directed graph. We adopt the idea of conceptual graphs to …...

AbstractThere has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text …...

AbstractRelation Extraction is a fundamental NLP task. In this paper we investigate the impact of underlying text representation on the performance of neural classification models in the task of Brand-Product …...

AbstractThe task of Entity linking, which aims at associating an entity mention with a unique entity in a knowledge base (KB), is useful for advanced Information Extraction tasks such as …...

AbstractArgument mining systems often consider contextual information, i.e. information outside of an argumentative discourse unit, when trained to accomplish tasks such as argument component identification, classification, and relation extraction. However, …...

AbstractDistant supervision (DS) has been widely adopted to generate auto-labeled data for sentence-level relation extraction (RE) and achieved great results. However, the existing success of DS cannot be directly transferred …...

AbstractWe present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue. We further offer DialogRE …...

AbstractRelation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models …...

AbstractGeneral purpose relation extraction has recently seen considerable gains in part due to a massively data-intensive distant supervision technique from Soares et al. (2019) that produces state-of-the-art results across many …...

AbstractSuppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow …...

AbstractThis paper studies the task of Relation Extraction (RE) that aims to identify the semantic relations between two entity mentions in text. In the deep learning models for RE, it …...

AbstractThe process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior. We identify failure modes of SOTA …...

AbstractNatural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures. In this paper, …...

AbstractWe propose a contextualised graph convolution network over multiple dependency-based sub-graphs for relation extraction. A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked …...

AbstractWrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction. Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but …...

AbstractSupervised models for Relation Extraction (RE) typically require human-annotated training data. Due to the limited size, the human-annotated data is usually incapable of covering diverse relation expressions, which could limit …...

AbstractIn recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments …...

AbstractNeural models have achieved remarkable success on relation extraction (RE) benchmarks. However, there is no clear understanding what information in text affects existing RE models to make decisions and how …...

AbstractDespite efforts to distinguish three different evaluation setups (Bekoulis et al., 2018), numerous end-to-end Relation Extraction (RE) articles present unreliable performance comparison to previous work. In this paper, we first …...

AbstractWe propose a newly annotated dataset for information extraction on recipes. Unlike previous approaches to machine comprehension of procedural texts, we avoid a priori pre-defining domain-specific predicates to recognize (e.g., …...

AbstractRecent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Efforts thus far have focused …...

AbstractResearch on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered. In this work, we present DeftEval, a SemEval …...

AbstractSpan-based joint extraction models have shown their efficiency on entity recognition and relation extraction. These models regard text spans as candidate entities and span tuples as candidate relation tuples. Span …...

AbstractTACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE). But, even with recent advances in unsupervised pre-training and knowledge enhanced neural RE, models still …...

AbstractIn recent years, distantly-supervised relation extraction has achieved a certain success by using deep neural networks. Distant Supervision (DS) can automatically generate large-scale annotated data by aligning entity pairs from …...

AbstractNamed entity recognition and relation extraction are two important fundamental problems. Joint learning algorithms have been proposed to solve both tasks simultaneously, and many of them cast the joint task …...

AbstractWe show that state-of-the-art self-supervised language models can be readily used to extract relations from a corpus without the need to train a fine-tuned extractive head. We introduce RE-Flex, a …...

AbstractBuilding predictive models for information extraction from text, such as named entity recognition or the extraction of semantic relationships between named entities in text, requires a large corpus of annotated …...

AbstractDistantly supervised datasets for relation extraction mostly focus on sentence-level extraction, and they cover very few relations. In this work, we propose cross-document relation extraction, where the two entities of …...

AbstractThis paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task it only identifies the named entities which are related to a specific …...

AbstractOpen Relation Extraction (OpenRE) aiming to extract relational facts from open-domain cor-pora is a sub-task of Relation Extraction and a crucial upstream process for many other NLPtasks. However various previous …...

AbstractRelation extraction is a subtask of natural langage processing that has seen many improvements in recent years, with the advent of complex pre-trained architectures. Many of these state-of-the-art approaches are …...

AbstractThe advent of neural-networks in NLP brought with it substantial improvements in supervised relation extraction. However, obtaining a sufficient quantity of training data remains a key challenge. In this work …...

AbstractThe journey of reducing noise from distant supervision (DS) generated training data has been started since the DS was first introduced into the relation extraction (RE) task. For the past …...

AbstractExisting works on information extraction (IE) have mainly solved the four main tasks separately (entity mention recognition, relation extraction, event trigger detection, and argument extraction), thus failing to benefit from …...

AbstractSyntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. …...

AbstractMuch recent work suggests that incorporating syntax information from dependency trees can improve task-specific transformer models. However, the effect of incorporating dependency tree information into pre-trained transformer models (e.g., BERT) …...

Natural language inference (NLI) is formu- lated as a unified framework for solving vari- ous NLP problems such as relation extraction, question answering, summarization, etc. It has been studied intensively …...

AbstractDocument-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level …...

AbstractTextual information extraction is a typical research topic in the NLP community. Several NLP tasks such as named entity recognition and relation extraction between entities have been well-studied in previous …...

Identifying relations from dialogues is more challenging than traditional sentence-level re- lation extraction (RE), since the difficulties of speaker information representation and the long-range semantic reasoning. Despite the successful efforts, …...

AbstractPrevious works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e.,semantic entity), while the relations in-between are largely unexplored. …...

AbstractPre-trained Language Models (PLMs) have shown superior performance on various downstream Natural Language Processing (NLP) tasks. However, conventional pre-training objectives do not explicitly model relational facts in text, which are …...

AbstractDistantly supervised models are very popular for relation extraction since we can obtain a large amount of training data using the distant supervision method without human annotation. In distant supervision, …...

We present a novel method for relation extrac- tion (RE) from a single sentence, mapping the sentence and two given entities to a canon- ical fact in a knowledge graph …...

AbstractThe key challenge of question answering over knowledge bases (KBQA) is the inconsistency between the natural language questions and the reasoning paths in the knowledge base (KB). Recent graph-based KBQA …...

AbstractPublic datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). However, the presence of overlap between the train …...

AbstractExtracting relations across large text spans has been relatively underexplored in NLP, but it is particularly important for high-value domains such as biomedicine, where obtaining high recall of the latest …...

AbstractUniversal schema (USchema) assumes that two sentence patterns that share the same entity pairs are similar to each other. This assumption is widely adopted for solving various types of relation …...

AbstractThere is an incredible amount of information available in the form of textual documents due to the growth of information sources. In order to get the information into an actionable …...

AbstractIn this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated …...

Syntactic and semantic structure directly re- flect relations expressed by the text at hand and are thus very useful for the relation extrac- tion (RE) task. Their symbolic nature allows …...

Relation extraction is an important task in in- formation extraction and retrieval that aims to extract relations among the given entities from running texts. To achieve a good performance for …...

AbstractMost recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the …...

AbstractDistantly supervision automatically generates plenty of training samples for relation extraction. However, it also incurs two major problems: noisy labels and imbalanced training data. Previous works focus more on reducing …...

AbstractDistant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications that need the exact relation type. …...

AbstractUnsupervised relation extraction works by clustering entity pairs that have the same relations in the text. Some existing variational autoencoder (VAE)-based approaches train the relation extraction model as an encoder …...

AbstractExtracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses. The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research …...

AbstractWhile relation extraction is an essential task in knowledge acquisition and representation, and new-generated relations are common in the real world, less effort is made to predict unseen relations that …...

Abstract“Named entity recognition and relation extraction are core sub-tasks of relational triple extraction. Recent studies have used parameter sharing or joint decoding to create interaction between these two tasks. However, …...

AbstractRelational web-tables are significant sources of structural information that are widely used for relation extraction and population of facts into knowledge graphs. To transform the web-table data into knowledge, we …...

Abstract“Supervised learning is a classic paradigm of relation extraction (RE). However, a well-performing model can still confidently make arbitrarily wrong predictions when exposed to samples of unseen relations. In this …...

AbstractIn this paper, we present an end-to-end joint entity and relation extraction approach based on transformer-based language models. We apply the model to the task of linking mathematical symbols to …...

A vehicle recall system is a process of recall- ing and repairing vehicles with defective de- signs or potential for accidents and failures. The recall document concisely explains the circumstances …...

AbstractBi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG).However, current architectures suffer from two drawbacks. They either …...

AbstractThe Relation Extraction (RE) is an important basic Natural Language Processing (NLP) for many applications, such as search engines, recommender systems, question-answering systems and others. There are many studies in …...

AbstractDocument-level relation extraction (DocRE) aims to extract semantic relations among entity pairs in a document. Typical DocRE methods blindly take the full document as input, while a subset of the …...

AbstractModern approaches in Natural Language Processing (NLP) require, ideally, large amounts of labelled data for model training. However, new language resources, for example, for Named Entity Recognition (NER), Co-reference Resolution …...

AbstractRelation extraction (RE) is a sub-field of information extraction, which aims to extract the relation between two given named entities (NEs) in a sentence and thus requires a good understanding …...

AbstractRecent relation extraction (RE) works have shown encouraging improvements by conducting contrastive learning on silver labels generated by distant supervision before fine-tuning on gold labels. Existing methods typically assume all …...

AbstractCurrent approaches for fixing systematic problems in NLP models (e.g., regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts. In contrast, humans often provide …...

AbstractWe present a generic and trend-aware curriculum learning approach that effectively integrates textual and structural information in text graphs for relation extraction between entities, which we consider as node pairs …...

AbstractThe dialogue-based relation extraction (DialogRE) task aims to predict the relations between argument pairs that appear in dialogue. Most previous studies utilize fine-tuning pre-trained language models (PLMs) only with extensive …...

AbstractRelation extraction (RE) is an important natural language processing task that predicts the relation between two given entities, where a good understanding of the contextual information is essential to achieve …...

AbstractIn the field of Japanese medical information extraction, few analyzing tools are available and relation extraction is still an under-explored topic. In this paper, we first propose a novel relation …...

AbstractThis paper describes our system in the SemEval-2022 Task 12: ‘linking mathematical symbols to their descriptions’, achieving first on the leaderboard for all the subtasks comprising named entity extraction (NER) …...

AbstractEvent trigger detection, entity mention recognition, event argument extraction, and relation extraction are the four important tasks in information extraction that have been performed jointly (Joint Information Extraction - JointIE) …...

AbstractAs digitized traditional cultural heritage documents have rapidly increased, resulting in an increased need for preservation and management, practical recognition of entities and typification of their classes has become essential. …...

AbstractSolving math word problems requires deductive reasoning over the quantities in the text. Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical expressions without explicitly …...

AbstractRelation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used to tackle the …...

AbstractWe target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and …...

AbstractConventional “closed-world” information extraction (IE) approaches rely on human ontologies to define the scope for extraction. As a result, such approaches fall short when applied to new domains. This calls …...

AbstractThis paper presents a new task of predicting the coverage of a text document for relation extraction (RE): Does the document contain many relational tuples for a given entity? Coverage …...

AbstractText mining and information extraction for the medical domain has focused on scientific text generated by researchers. However, their access to individual patient experiences or patient-doctor interactions is limited. On …...

AbstractRelation extraction (RE) has achieved remarkable progress with the help of pre-trained language models. However, existing RE models are usually incapable of handling two situations: implicit expressions and long-tail relation …...

AbstractWe present a simple yet effective self-training approach, named as STAD, for low-resource relation extraction. The approach first classifies the auto-annotated instances into two groups: confident instances and uncertain instances, …...

AbstractWe study a new problem setting of information extraction (IE), referred to as text-to-table. In text-to-table, given a text, one creates a table or several tables expressing the main content …...

AbstractRelation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is the spoken language, such as …...

AbstractRelation extraction (RE), which has relied on structurally annotated corpora for model training, has been particularly challenging in low-resource scenarios and domains. Recent literature has tackled low-resource RE by self-supervised …...

Abstract“Document Information Extraction (DIE) is a crucial task for extracting key information fromvisually-rich documents. The typical pipeline approach for this task involves Optical Charac-ter Recognition (OCR), serializer, Semantic Entity Recognition …...

AbstractDespite the extensive applications of relation extraction (RE) tasks in various domains, little has been explored in the historical context, which contains promising data across hundreds and thousands of years. …...

AbstractWe propose an explainable approach for relation extraction that mitigates the tension between generalization and explainability by jointly training for the two goals. Our approach uses a multi-task learning architecture, …...

AbstractSemi-supervised learning has been an important approach to address challenges in extracting entities and relations from limited data. However, current semi-supervised works handle the two tasks (i.e., Named Entity Recognition …...

AbstractRecent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on the in-domain performance of several NLP tasks. We extend this work by exploring …...

AbstractRelation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English …...

AbstractRelation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. …...

AbstractRelation extraction (RE) is the core NLP task of inferring semantic relationships between entities from text. Standard supervised RE techniques entail training modules to tag tokens comprising entity spans and …...

AbstractDue to the semantic complexity of the Relation extraction (RE) task, obtaining high-quality human labelled data is an expensive and noisy process. To improve the sample efficiency of the models, …...

AbstractMany tasks in natural language processing require the extraction of relationship information for a given condition, such as event argument extraction, relation extraction, and task-oriented semantic parsing. Recent works usually …...

Abstract“Relation Extraction (RE) task aims to discover the semantic relation that holds between two entitiesand contributes to many applications such as knowledge graph construction and completion. Reinforcement Learning (RL) has …...

AbstractWe propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, …...